Determining Spatial Permeability From A Formation Tester

ABSTRACT

A method and system for determining a bed permeability. As disclosed, a fluid sampling tool may be disposed into a wellbore at a first location. The method may further include taking a drawdown and build up measurement with the fluid sampling tool, measuring a relative dip angle from the fluid sampling tool, calculating a bed anisotropy from the drawdown and build up measurement and the relative dip angle, calculating a bed mobility from the bed anisotropy, and calculating a bed permeability from the bed mobility and a viscosity.

BACKGROUND

Wells may be drilled at various depths to access and produce oil, gas, minerals, and other naturally occurring deposits from subterranean geological formations. The drilling of a well is typically accomplished with a drill bit that is rotated within the well to advance the well by removing topsoil, sand, clay, limestone, calcites, dolomites, or other materials.

During or after drilling operations, sampling operations may be performed to collect a representative sample of formation or reservoir fluids (e.g., hydrocarbons) to further evaluate drilling operations and production potential, or to detect the presence of certain gases or other materials in the formation that may affect well performance. Sampling operations may be performed by a fluid sampling tool.

During sampling operations, a fluid sampling tool is disposed in a wellbore and may take pressure measurements, calculate mobilities that are horizontal to the wellbore and associated formations during sampling operations. As a basis to this measurement, current technology assumes that the fluid sampling tool measures parallel to a sand bed and the permeability is directly representative of both the reservoir and geology of the formation. This, however, is inaccurate and realistic. Generally, sand bedding planes may be at an angle different to the horizontal direction of a formation tester, affecting the measurement and consequently the permeability. Misrepresenting permeability may have drastic effects in estimating the production delivery of a field and result in further questions on the economic viability of a prospect.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of certain embodiments will be more readily appreciated when considered in conjunction with the accompanying figures. The figures are not to be construed as limiting any of the preferred embodiments.

FIG. 1 illustrates a schematic view of a well in which an example embodiment of a fluid sample system is deployed;

FIG. 2 illustrates a schematic view of another well in which an example embodiment of a fluid sample system is deployed;

FIG. 3 illustrates a schematic view of a chipset in an information handling system;

FIG. 4 illustrates the chipset in communication with other components of the information handling system;

FIG. 5 illustrates a schematic view of a cloud-based system;

FIG. 6 illustrates a neural network;

FIG. 7 illustrates a schematic view of an example embodiment of a fluid sampling tool;

FIG. 8 illustrates a fluid sampling tool disposed in a wellbore during a sampling operation;

FIGS. 9-11 illustrate different bending curve fluid models that are applicable to sampling operations;

FIG. 12 is a graph that illustrates the mobility of a bed relative to the dip angle for various ratios of bed cross sectional area to probe cross sectional area;

FIG. 13 illustrates the variables M_(Bed), M_(x), and θ in relation to fluid sampling tool;

FIG. 14 is a workflow for dip alignment permeability of a bed;

FIG. 15 illustrates θ is relative dip angle;

FIG. 16 illustrates an example of two fluid sampling tools disposed on a conveyance orthogonal to each other that are disposed in formation;

FIG. 17 illustrates different measurements made by the two fluid sampling tools in FIG. 16 ; and

FIG. 18 illustrates a workflow for identifying spatial permeability.

DETAILED DESCRIPTION

The present disclosure relates to subterranean operations and, more particularly, embodiments disclosed herein provide methods and systems for representing a permeability calculation parallel to a bedding plane. This may be performed utilizing prior knowledge of geology, reservoir, and how the well is drilled. Methods and systems described below may be utilized to enhance current permeability calculations by incorporating theories of fluid mechanics to account for changes in the formation, which may be utilized to measure permeability of a bed, strata, and/or formation.

FIG. 1 is a schematic diagram of downhole fluid sampling tool 100 on a conveyance 102. As illustrated, wellbore 104 may extend through subterranean formation 106. In examples, reservoir fluid may be contaminated with well fluid (e.g., drilling fluid) from wellbore 104. As described herein, the fluid sample may be analyzed to determine fluid contamination and other fluid properties of the reservoir fluid. As illustrated, a wellbore 104 may extend through subterranean formation 106. While the wellbore 104 is shown extending generally vertically into the subterranean formation 106, the principles described herein are also applicable to wellbores that extend at an angle through the subterranean formation 106, such as horizontal and slanted wellbores. For example, although FIG. 1 shows a vertical or low inclination angle well, high inclination angle or horizontal placement of the well and equipment is also possible. It should further be noted that while FIG. 1 generally depicts a land-based operation, those skilled in the art will readily recognize that the principles described herein are equally applicable to subsea operations that employ floating or sea-based platforms and rigs, without departing from the scope of the disclosure.

As illustrated, a hoist 108 may be used to run downhole fluid sampling tool 100 into wellbore 104. Hoist 108 may be disposed on a vehicle 110. Hoist 108 may be used, for example, to raise and lower conveyance 102 in wellbore 104. While hoist 108 is shown on vehicle 110, it should be understood that conveyance 102 may alternatively be disposed from a hoist 108 that is installed at surface 112 instead of being located on vehicle 110. Downhole fluid sampling tool 100 may be suspended in wellbore 104 on conveyance 102. Other conveyance types may be used for conveying downhole fluid sampling tool 100 into wellbore 104, including coiled tubing and wired drill pipe, for example. Downhole fluid sampling tool 100 may comprise a tool body 114, which may be elongated as shown on FIG. 1 . Tool body 114 may be any suitable material, including without limitation titanium, stainless steel, alloys, plastic, combinations thereof, and the like. Downhole fluid sampling tool 100 may further comprise one or more sensors 116 for measuring properties of the fluid sample, reservoir fluid, wellbore 104, subterranean formation 106, or the like. In examples, downhole fluid sampling tool 100 may also comprise a fluid analysis module 118, which may be operable to process information regarding fluid sample, as described below. The downhole fluid sampling tool 100 may be used to collect fluid samples from subterranean formation 106 and may obtain and separately store different fluid samples from subterranean formation 106.

In examples, fluid analysis module 118 may comprise at least one a sensor that may continuously monitor a fluid such as a reservoir fluid, formation fluid, wellbore fluid, or formation nonnative fluids such as drilling fluid filtrate. Such monitoring may take place in a fluid flow line or a formation tester probe such as a pad or packer or may be able to make measurements investigating the formation including measurements into the formation. Such sensors comprise optical sensors, acoustic sensors, electromagnetic sensors, conductivity sensors, resistivity sensors, selective electrodes, density sensors, mass sensors, thermal sensors, chromatography sensors, viscosity sensors, bubble point sensors, fluid compressibility sensors, flow rate sensors, pressure sensors, nuclear magnetic resonance (NMR) sensors. Sensors may measure a contrast between drilling fluid filtrate properties and formation fluid properties. Fluid analysis module 118 may be operable to derive properties and characterize the fluid sample. By way of example, fluid analysis module 118 may measure absorption, transmittance, or reflectance spectra and translate such measurements into component concentrations of the fluid sample, which may be lumped component concentrations, as described above. The fluid analysis module 118 may also measure gas-to-oil ratio, fluid composition, water cut, live fluid density, live fluid viscosity, formation pressure, and formation temperature and fluid composition. Fluid analysis module 118 may also be operable to determine fluid contamination of the fluid sample and may comprise any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. The absorption, transmittance, or reflectance spectra absorption, transmittance, or reflectance spectra may be measured with sensors 116 by way of standard operations. For example, fluid analysis module 118 may comprise random access memory (RAM), one or more processing units, such as a central processing unit (CPU), or hardware or software control logic, ROM, and/or other types of nonvolatile memory. Fluid analysis module 118 and fluid sampling tool 100 may be communicatively coupled via communication link 120 with information handling system 122.

Any suitable technique may be used for transmitting signals from the downhole fluid sampling tool 100 to the surface 112. As illustrated, a communication link 120 (which may be wired or wireless, for example) may be provided that may transmit data from downhole fluid sampling tool 100 to an information handling system 122 at surface 112. Information handling system 122 may comprise a processing unit 124, a monitor 126, an input device 128 (e.g., keyboard, mouse, etc.), and/or computer media 130 (e.g., optical disks, magnetic disks) that can store code representative of the methods described herein. Information handling system 122 may act as a data acquisition system and possibly a data processing system that analyzes information from downhole fluid sampling tool 100. For example, information handling system 122 may process the information from downhole fluid sampling tool 100 for determination of fluid contamination. The information handling system 122 may also determine additional properties of the fluid sample (or reservoir fluid), such as component concentrations, pressure-volume-temperature properties (e.g., bubble point, phase envelop prediction, etc.) based on the fluid characterization. This processing may occur at surface 112 in real-time. Alternatively, the processing may occur downhole hole or at surface 112 or another location after recovery of downhole fluid sampling tool 100 from wellbore 104. Alternatively, the processing may be performed by an information handling system in wellbore 104, such as fluid analysis module 118. The resultant fluid contamination and fluid properties may then be transmitted to surface 112, for example, in real-time.

Referring now to FIG. 2 , a schematic diagram of downhole fluid sampling tool 100 disposed on a drill string 200 in a drilling operation. Downhole fluid sampling tool 100 may be used to obtain a fluid sample, for example, a fluid sample of a reservoir fluid from subterranean formation 106. The reservoir fluid may be contaminated with well fluid (e.g., drilling fluid) from wellbore 104. As described herein, the fluid sample may be analyzed to determine fluid contamination and other fluid properties of the reservoir fluid. As illustrated, a wellbore 104 may extend through subterranean formation 106. While the wellbore 104 is shown extending generally vertically into the subterranean formation 106, the principles described herein are also applicable to wellbores that extend at an angle through the subterranean formation 106, such as horizontal and slanted wellbores. For example, although FIG. 2 shows a vertical or low inclination angle well, high inclination angle or horizontal placement of the well and equipment is also possible. It should further be noted that while FIG. 2 generally depicts a land-based operation, those skilled in the art will readily recognize that the principles described herein are equally applicable to subsea operations that employ floating or sea-based platforms and rigs, without departing from the scope of the disclosure.

As illustrated, a drilling platform 202 may support a derrick 204 having a traveling block 206 for raising and lowering drill string 200. Drill string 200 may comprise, but is not limited to, drill pipe and coiled tubing, as generally known to those skilled in the art. A kelly 208 may support drill string 200 as it may be lowered through a rotary table 210. A drill bit 212 may be attached to the distal end of drill string 200 and may be driven either by a downhole motor and/or via rotation of drill string 200 from the surface 112. Without limitation, drill bit 212 may comprise roller cone bits, PDC bits, natural diamond bits, any hole openers, reamers, coring bits, and the like. As drill bit 212 rotates, it may create and extend wellbore 104 that penetrates various subterranean formations 106. A pump 214 may circulate drilling fluid through a feed pipe 216 to kelly 208, downhole through interior of drill string 200, through orifices in drill bit 212, back to surface 112 via annulus 218 surrounding drill string 200, and into a retention pit 220.

Drill bit 212 may be just one piece of a downhole assembly that may comprise one or more drill collars 222 and downhole fluid sampling tool 100. Downhole fluid sampling tool 100, which may be built into drill collars 222 may gather measurements and fluid samples as described herein. One or more of the drill collars 222 may form a tool body 114, which may be elongated as shown on FIG. 2 . Tool body 114 may be any suitable material, including without limitation titanium, stainless steel, alloys, plastic, combinations thereof, and the like. Downhole fluid sampling tool 100 may be similar in configuration and operation to downhole fluid sampling tool 100 shown on FIG. 1 except that FIG. 2 shows downhole fluid sampling tool 100 disposed on drill string 200. Alternatively, the sampling tool may be lowered into the wellbore after drilling operations on a wireline.

Downhole fluid sampling tool 100 may further comprise one or more sensors 116 for measuring properties of the fluid sample reservoir fluid, wellbore 104, subterranean formation 106, or the like. The one or more sensors 116 may be disposed within fluid analysis module 118. In examples, more than one fluid analysis module may be disposed on drill string 200. The properties of the fluid are measured as the fluid passes from the formation through the tool and into either the wellbore or a sample container. As fluid is flushed in the near wellbore region by the mechanical pump, the fluid that passes through the tool generally reduces in drilling fluid filtrate content, and generally increases in formation fluid content. The downhole fluid sampling tool 100 may be used to collect a fluid sample from subterranean formation 106 when the filtrate content has been determined to be sufficiently low. Sufficiently low depends on the purpose of sampling. For some laboratory testing below 10% drilling fluid contamination is sufficiently low, and for other testing below 1% drilling fluid filtrate contamination is sufficiently low. Sufficiently low also depends on the nature of the formation fluid such that lower requirements are generally needed, the lighter the oil as designated with either a higher GOR or a higher API gravity. Sufficiently low also depends on the rate of cleanup in a cost benefit analysis since longer pumpout times required to incrementally reduce the contamination levels may have prohibitively large costs. As previously described, the fluid sample may comprise a reservoir fluid, which may be contaminated with a drilling fluid or drilling fluid filtrate. Downhole fluid sampling tool 100 may obtain and separately store different fluid samples from subterranean formation 106 with fluid analysis module 118. Fluid analysis module 118 may operate and function in the same manner as described above. However, storing of the fluid samples in the downhole fluid sampling tool 100 may be based on the determination of the fluid contamination. For example, if the fluid contamination exceeds a tolerance, then the fluid sample may not be stored. If the fluid contamination is within a tolerance, then the fluid sample may be stored in the downhole fluid sampling tool 100. In examples, contamination may be defined within fluid analysis module 118.

As previously described, information from downhole fluid sampling tool 100 may be transmitted to an information handling system 122, which may be located at surface 112. As illustrated, communication link 120 (which may be wired or wireless, for example) may be provided that may transmit data from downhole fluid sampling tool 100 to an information handling system 122 at surface 112. Information handling system 122 may comprise a processing unit 124, a monitor 126, an input device 128 (e.g., keyboard, mouse, etc.), and/or computer media 130 (e.g., optical disks, magnetic disks) that may store code representative of the methods described herein. In addition to, or in place of processing at surface 112, processing may occur downhole (e.g., fluid analysis module 118). In examples, information handling system 122 may perform computations to estimate asphaltenes within a fluid sample.

FIG. 3 illustrates an example information handling system 122 which may be employed to perform various steps, methods, and techniques disclosed herein. Persons of ordinary skill in the art will readily appreciate that other system examples are possible. As illustrated, information handling system 122 comprises a processing unit (CPU or processor) 302 and a system bus 304 that couples various system components including system memory 306 such as read only memory (ROM) 308 and random-access memory (RAM) 310 to processor 302. Processors disclosed herein may all be forms of this processor 302. Information handling system 122 may comprise a cache 312 of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 302. Information handling system 122 copies data from memory 306 and/or storage device 314 to cache 312 for quick access by processor 302. In this way, cache 312 provides a performance boost that avoids processor 302 delays while waiting for data. These and other modules may control or be configured to control processor 302 to perform various operations or actions. Other system memory 306 may be available for use as well. Memory 306 may comprise multiple different types of memory with different performance characteristics. It may be appreciated that the disclosure may operate on information handling system 122 with more than one processor 302 or on a group or cluster of computing devices networked together to provide greater processing capability. Processor 302 may comprise any general-purpose processor and a hardware module or software module, such as first module 316, second module 318, and third module 320 stored in storage device 314, configured to control processor 302 as well as a special-purpose processor where software instructions are incorporated into processor 302. Processor 302 may be a self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. Processor 302 may comprise multiple processors, such as a system having multiple, physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, processor 302 may comprise multiple distributed processors located in multiple separate computing devices but working together such as via a communications network. Multiple processors or processor cores may share resources such as memory 306 or cache 312 or may operate using independent resources. Processor 302 may comprise one or more state machines, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA (FPGA).

Each individual component discussed above may be coupled to system bus 304, which may connect each and every individual component to each other. System bus 304 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in ROM 308 or the like, may provide the basic routine that helps to transfer information between elements within information handling system 122, such as during start-up. Information handling system 122 further comprises storage devices 314 or computer-readable storage media such as a hard disk drive, a magnetic disk drive, an optical disk drive, tape drive, solid-state drive, RAM drive, removable storage devices, a redundant array of inexpensive disks (RAID), hybrid storage device, or the like. Storage device 314 may comprise software modules 316, 318, and 320 for controlling processor 302. Information handling system 122 may comprise other hardware or software modules. Storage device 314 is connected to the system bus 304 by a drive interface. The drives and the associated computer-readable storage devices provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for information handling system 122. In one aspect, a hardware module that performs a particular function comprises the software component stored in a tangible computer-readable storage device in connection with the necessary hardware components, such as processor 302, system bus 304, and so forth, to carry out a particular function. In another aspect, the system may use a processor and computer-readable storage device to store instructions which, when executed by the processor, cause the processor to perform operations, a method or other specific actions. The basic components and appropriate variations may be modified depending on the type of device, such as whether information handling system 122 is a small, handheld computing device, a desktop computer, or a computer server. When processor 302 executes instructions to perform “operations”, processor 302 may perform the operations directly and/or facilitate, direct, or cooperate with another device or component to perform the operations.

As illustrated, information handling system 122 employs storage device 314, which may be a hard disk or other types of computer-readable storage devices which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memories (RAMs) 310, read only memory (ROM) 308, a cable containing a bit stream and the like, may also be used in the exemplary operating environment. Tangible computer-readable storage media, computer-readable storage devices, or computer-readable memory devices, expressly exclude media such as transitory waves, energy, carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with information handling system 122, an input device 322 represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. Additionally, input device 322 may take in data from one or more sensors 136, discussed above. An output device 324 may also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with information handling system 122. Communications interface 326 generally governs and manages the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic hardware depicted may easily be substituted for improved hardware or firmware arrangements as they are developed.

As illustrated, each individual component described above is depicted and disclosed as individual functional blocks. The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software and hardware, such as a processor 302, that is purpose-built to operate as an equivalent to software executing on a general-purpose processor. For example, the functions of one or more processors presented in FIG. 3 may be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may comprise microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM) 308 for storing software performing the operations described below, and random-access memory (RAM) 310 for storing results. Very large-scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general-purpose DSP circuit, may also be provided.

The logical operations of the various methods, described below, are implemented as: (1) a sequence of computer implemented steps, operations, or procedures running on a programmable circuit within a general use computer, (2) a sequence of computer implemented steps, operations, or procedures running on a specific-use programmable circuit; and/or (3) interconnected machine modules or program engines within the programmable circuits. Information handling system 122 may practice all or part of the recited methods, may be a part of the recited systems, and/or may operate according to instructions in the recited tangible computer-readable storage devices. Such logical operations may be implemented as modules configured to control processor 302 to perform particular functions according to the programming of software modules 316, 318, and 320.

In examples, one or more parts of the example information handling system 122, up to and including the entire information handling system 122, may be virtualized. For example, a virtual processor may be a software object that executes according to a particular instruction set, even when a physical processor of the same type as the virtual processor is unavailable. A virtualization layer or a virtual “host” may enable virtualized components of one or more different computing devices or device types by translating virtualized operations to actual operations. Ultimately however, virtualized hardware of every type is implemented or executed by some underlying physical hardware. Thus, a virtualization computer layer may operate on top of a physical computer layer. The virtualization computer layer may comprise one or more virtual machines, an overlay network, a hypervisor, virtual switching, and any other virtualization application.

FIG. 4 illustrates an example information handling system 122 having a chipset architecture that may be used in executing the described method and generating and displaying a graphical user interface (GUI). Information handling system 122 is an example of computer hardware, software, and firmware that may be used to implement the disclosed technology. Information handling system 122 may comprise a processor 302, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 302 may communicate with a chipset 400 that may control input to and output from processor 302. In this example, chipset 400 outputs information to output device 324, such as a display, and may read and write information to storage device 314, which may comprise, for example, magnetic media, and solid-state media. Chipset 400 may also read data from and write data to RAM 310. A bridge 402 for interfacing with a variety of user interface components 404 may be provided for interfacing with chipset 400. Such user interface components 404 may comprise a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to information handling system 122 may come from any of a variety of sources, machine generated and/or human generated.

Chipset 400 may also interface with one or more communication interfaces 326 that may have different physical interfaces. Such communication interfaces may comprise interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein may comprise receiving ordered datasets over the physical interface or be generated by the machine itself by processor 302 analyzing data stored in storage device 314 or RAM 310. Further, information handling system 122 receives inputs from a user via user interface components 404 and executes appropriate functions, such as browsing functions by interpreting these inputs using processor 302.

In examples, information handling system 122 may also comprise tangible and/or non-transitory computer-readable storage devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices may be any available device that may be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which may be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network, or another communications connection (either hardwired, wireless, or combination thereof), to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be comprised within the scope of the computer-readable storage devices.

Computer-executable instructions comprise, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also comprise program modules that are executed by computers in stand-alone or network environments. Generally, program modules comprise routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

In additional examples, methods may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Examples may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. During drilling operations information handling system 122 may process different types of the real time data which may be utilized to create an asphaltene onset pressure map (AOP).

FIG. 5 illustrates an example of one arrangement of resources in a computing network 500 that may employ the processes and techniques described herein, although many others are of course possible. As noted above, an information handling system 122, as part of their function, may utilize data, which comprises files, directories, metadata (e.g., access control list (ACLS) creation/edit dates associated with the data, etc.), and other data objects. The data on the information handling system 122 is typically a primary copy (e.g., a production copy). During a copy, backup, archive or other storage operation, information handling system 122 may send a copy of some data objects (or some components thereof) to a secondary storage computing device 504 by utilizing one or more data agents 502.

A data agent 502 may be a desktop application, website application, or any software-based application that is run on information handling system 122. As illustrated, information handling system 122 may be disposed at any rig site (e.g., referring to FIG. 1 ) or repair and manufacturing center. Data agent 502 may communicate with a secondary storage computing device 504 using communication protocol 508 in a wired or wireless system. Communication protocol 508 may function and operate as an input to a website application. In the website application, field data related to pre- and post-operations, generated DTCs, notes, and the like may be uploaded. Additionally, information handling system 122 may utilize communication protocol 508 to access processed measurements, operations with similar DTCs, troubleshooting findings, historical run data, and/or the like. This information is accessed from secondary storage computing device 504 by data agent 502, which is loaded on information handling system 122.

Secondary storage computing device 504 may operate and function to create secondary copies of primary data objects (or some components thereof) in various cloud storage sites 506A-N. In examples, cloud storage sites 506A-N may be one or more databases located on site or offsite. Additionally, secondary storage computing device 504 may run determinative algorithms on data uploaded from one or more information handling systems 122, discussed further below. Communications between the secondary storage computing devices 504 and cloud storage sites 506A-N may utilize REST protocols (Representational state transfer interfaces) that satisfy basic C/R/U/D semantics (Create/Read/Update/Delete semantics), or other hypertext transfer protocol (“HTTP”)-based or file-transfer protocol (“FTP”)-based protocols (e.g., Simple Object Access Protocol).

In conjunction with creating secondary copies in cloud storage sites 506A-N, the secondary storage computing device 504 may also perform local content indexing and/or local object-level, sub-object-level or block-level deduplication when performing storage operations involving various cloud storage sites 506A-N. Cloud storage sites 506A-N may further record and maintain DTC code logs for each downhole operation or run, map DTC codes, store repair and maintenance data, store operational data, and/or provide outputs from determinative algorithms that are fun at cloud storage sites 506A-N. This type of network may be utilized to an asphaltene onset pressure map (AOP).

FIG. 6 illustrates neural network (NN) 600. NN 600 may operate utilizing one or more information handling systems 122 (e.g., referring to FIGS. 1 and 2 ) on computing network 500. Although a NN is illustrated, multiple models may be used with input output structures. These models may comprise flexible empirical models such as NN, gaussian processing methods, kriging methods, evolutionary methods such as genetic algorithms, classification methods, clustering methods empirical methods, or physics-based methods such as equations of state, thermodynamic models, geological, geochemistry, or chemistry models, or kinetic models or any combinations therein including recursive combinations of similar or dissimilar models and iterative model combinations. A NN 600 is an artificial neural network with one or more hidden layers 602 between input layer 604 and output layer 606. In examples, NN 600 may be software on a single information handling system 122. In other examples, NN 600 may software running on multiple information handling systems 122 connected wirelessly and/or by a hard-wired connection in a network of multiple information handling systems 122.

Herein, NN 600 may be applied in a wide array of implementations. For example, NN 600 may be modeled for forming an AOP map, reservoir simulation, production decisions, or single AOP determinations. UAOP, the ARFO, or the BP from the gravimetric test are used in a NN model to identify the first AOP or the second AOP.

As such, input layer 604 may comprise any number of inputs 608. Inputs 608 may comprise properties of fluid and/or fluid formations such as physical properties (bulk or molecular) such as density, index of refraction, compressibility, bubble point, phase and/or other phase behavior properties measured by sampling tool 100. In examples, inputs may also comprise transport properties such as viscosity or thermal conductivity. Fluid analysis modules 118 may determine optical, chromatographic, mass spectrometry, density sensor, viscosity sensor, phase change apparatus compressibility sensor resistivity sensor, capacitance or dielectric sensor acoustic sensor, or combinations therein. Additionally, inputs 608 may also comprise chemical properties including composition i.e., hydrocarbon composition (methane, ethane propane, butane, pentane, hexane, higher hydrocarbons) and or chemical classes such as but not limited to Saturates, Aromatics, Resins or Asphaltenes chemical classes, and their respective concentrations of the various components, pH, eH, chemical potential, reactivity, fluid compatibility, and/or scaling potential. Fluid analysis modules 118 may determine optical, chromatographic, mass spectrometry, density sensor, viscosity sensor, phase change apparatus compressibility sensor resistivity sensor, capacitance or dielectric sensor acoustic sensor, or combinations therein. In other examples, inputs may comprise raw sensor measurements such as temperature, pressure, optical information, acoustic information, and/or electromagnetic information. Fluid analysis modules 118 may determine optical, chromatographic, mass spectrometry, density sensor, viscosity sensor, phase change apparatus compressibility sensor resistivity sensor, capacitance or dielectric sensor acoustic sensor, or combinations therein. In examples, output layer 606 may form outputs 610. Outputs 610 may comprise other unmeasured or less well measured physical or chemical properties, and/or correlated sensor measurements. For instance, outputs 610 may comprise scaling potential, or asphaltene onset pressure if not directly measured. Alternatively, the model may provide outputs 610 for enhanced resolution, precision or accuracy refinement of a measured property such as bubble point, or asphaltene onset pressure which may be comprised as an input 608 but refined as an enhanced measurement as an output 610 in output layer 606. Any of the inputs 608 or outputs 610 may be from the current well being evaluated or analogue wells which may be in the field, in the basis, or not so if other characteristics such as but not limited to formation type or formation fluid provide a basis for analogy. During operations, inputs 608 data are given to neurons 612 in input layer 604. Neurons 612, 614, and 616 are defined as individual or multiple information handling systems 122 connected in a network, which may compute information to make drilling, completion or production decisions such as but not limited how to drill the well, where to drill the well, how to complete a well, or where to complete a well, or how to produce a well, or where to produce a well. Any of computations may be from the current well being evaluated or analogue wells which may be in the field, in the basis, or not so if other characteristics such as but not limited to formation type or formation fluid provide a basis for analogy. The output from neurons 612 may be transferred to one or more neurons 614 within one or more hidden layers 602. Hidden layers 602 comprises one or more neurons 614 connected in a network that further process information from neurons 612. The number of hidden layers 602 and neurons 612 in hidden layer 602 may be determined by personnel that design NN 600. Hidden layers 602 is defined as a set of information handling system 122 assigned to specific processing. Hidden layers 602 spread computation to neurons 614, which may allow for faster computing, processing, training, and learning by NN 600. Output layers 606 may combine the processing in hidden layers 602, using neurons 616, to form an asphaltene onset pressure (AOP). By any of the modeling methods, output layers 606, wherein other methods may use different layer or subfunction structuring, may be coordinated such that simultaneously an AOP may be provided for different outputs each corresponding to a different depths or lateral distance across a field or distance from an injecting well, temperature or other state condition comprising at least formation or concentration of materials. Multiple outputs may be coordinated wherein the multiple outputs are different but related parameters which may comprise but is not limited to asphaltene onset pressure, and asphaltene stability index, either static for a single state, or as a function independent variable such as but not limited to depth or lateral distance across a field or distance from an injecting well or of state variables such as but not limited to temperature.

FIG. 7 illustrates a schematic of fluid sampling tool 100. As illustrated, fluid sampling tool 100 comprises a power telemetry section 702 through which fluid sampling tool 100 may communicate with other actuators and sensors in a conveyance (e.g., conveyance 102 on FIG. 1 or drill string 200 on FIG. 2 ), the conveyance's communications system, such as information handling system 122 (e.g., referring to FIG. 1 ). In examples, power telemetry section 702 may also be a port through which the various actuators (e.g., valves) and sensors (e.g., temperature and pressure sensors) in fluid sampling tool 100 may be controlled and monitored. In examples, power telemetry section 702 may comprise an additional information handling system 122 (not illustrated) that exercises the control and monitoring function. In one example, the control and monitoring function is performed by an information handling system 122 in another part of the drill string or fluid sampling tool 100 (not shown) or by an information handling system at surface 112.

Information from fluid sampling tool 100 may be gathered and/or processed by the information handling system 122 (e.g., referring to FIGS. 1 and 2 ). The processing may be performed real-time during data acquisition or after recovery of fluid sampling tool 100. Processing may alternatively occur downhole or may occur both downhole and at surface 112. In some examples, signals recorded by fluid sampling tool 100 may be conducted to information handling system by way of conveyance. Information handling system may process the signals, and the information contained therein may be displayed for an operator to observe and stored for future processing and reference. Information handling system may also contain an apparatus for supplying control signals and power to fluid sampling tool 100.

In examples, fluid sampling tool 100 may comprise one or more enhanced probe sections 704 and stabilizers 724. Each enhanced probe section may comprise a dual probe section 706 or a focus sampling probe section 708. Both of which may extract fluid from the reservoir and deliver said fluid to a channel 710 that extends from one end of fluid sampling tool 100 to the other. Without limitation, dual probe section 706 comprises two probes 712, 714 which may extend from fluid sampling tool 100 and press against the inner wall of wellbore 104 (e.g., referring to FIG. 1). Probe channels 716 and 718 may connect probe 712, 714 to channel 710 and allow for continuous fluid flow from the formation 106 to channel 710. A high-volume bidirectional pump 720 may be used to pump fluids from the formation, through probe channels 716, 718 and to channel 710. Alternatively, a low volume pump bi direction piston 722 may be used to remove reservoir fluid from the reservoir and house them for asphaltene measurements, discussed below. Two standoffs or stabilizers 724, 726 hold fluid sampling tool 100 in place as probes 712, 714 press against the wall of wellbore 104. In examples, probes 712, 714 and stabilizers 724, 726 may be retracted when fluid sampling tool 100 may be in motion and probes 712, 714 and stabilizers 724, 726 may be extended to sample the formation fluids at any suitable location in wellbore 104. As illustrated, probes 712, 714 may be replaced, or used in conjunction with, focus sampling probe section 708. Focus sampling prob section 708 may operate and function as discussed above for probes 712, 714 but with a single probe 728. Other probe examples may comprise, but are not limited to, oval probes, packers, or circumferential probes.

In examples, channel 710 may connect other parts and sections of fluid sampling tool 100 to each other. Additionally, a second high-volume bidirectional pump 730 may pump fluid through channel 710 to one or more multi-chamber sections 732, one or more fluid density modules 734, and/or one or more optics analyzers 736.

FIG. 8 illustrates fluid sampling tool 100 disposed in wellbore 104 during a sampling operation. Fluid sampling tool 100 may be utilized to measure horizontal permeability K_(x) as well as bed permeability K_(Bed). As illustrated, bed permeability may be aligned parallel to a bedding plane 800 of a bed 802 within formation 106. It should be noted, that in examples, both K_(x) as well as K_(Bed) may be equivalent when θ is zero. For this disclosure, θ is defined as a relative dip angle. As illustrated, θ may be measured as an angle originating from a horizontal plane 804 that moves through focus sampling probe 708 at landing location 806 into bed 802. The angle may traverse from horizontal plane 804 to bed boundary 808, which is disposed above focus sampling probe 708. By identifying and measuring θ, a bending curve fluid model may be created to determine mobility of a fluid within bed 802.

FIGS. 9-11 illustrate a bending curve fluid model 900. As illustrated, V₂ is a velocity of fluid at bedding plane 800 and V₁ is the velocity of fluid entering sampling probe section 708. Further illustrated are θ, relative dip angle, as discussed above, A₁, which is a cross-area of sampling probe section 708, P₁ is a pressure measured by sampling probe section 708 at a depth in wellbore 104, P₂ is pressure of fluid at bedding plane 800, A₂ is a cross-area of bedding plane 800 (i.e., an angle between sampling probe section 708 and bedding plane 800), F_(x) is force exerted by fluid onto bedding plane 800 in an x direction (at the bend), −F_(x) is force exerted by bedding plane 800 onto fluid in an x direction, F_(y) is a force exerted by fluid onto bedding plane 800 in a y direction (at the bend), and −F_(y) is force exerted by bedding plane 800 onto fluid in a y direction. In the disclosure, “at the bend” is defined as area and/or location where force transitions from F_(x) to F_(y), or vice versa. Using these measured variables, rate of change of momentum and conservation of energy may be calculated using the following equations:

$\begin{matrix} {{{{- P_{1}}A_{1}} + {P_{2}A_{2}{\cos(\theta)}} + F_{x}} = {\rho{Q\left( {{- V_{1}} + {V_{2}{\cos(\theta)}}} \right)}}} & (1) \end{matrix}$ $\begin{matrix} {{{- F_{y}} + {P_{2}A_{2}{\cos(\theta)}} + F_{x}} = {\rho{Q\left( {V_{2}{\sin(\theta)}} \right)}}} & (2) \end{matrix}$ $\begin{matrix} {{P_{1} + {\frac{1}{2}\rho V_{1}^{2}}} = {P_{2} + {\frac{1}{2}\rho V_{2}^{2}}}} & (3) \end{matrix}$ $\begin{matrix} {Q = {{V_{1}A_{1}} = {V_{2}A_{2}}}} & (4) \end{matrix}$ $\begin{matrix} {F_{R} = \sqrt{F_{x}^{2} + F_{y}^{2}}} & (5) \end{matrix}$

where Q is flow rate measured by fluid sampling tool 100 (e.g., referring to FIG. 1 ), and ρ is fluid density. FIG. 12 is a graph that illustrates the mobility of bed 802 (e.g., referring to FIG. 8 ) relative to θ for various ratios of bed cross sectional area to probe cross sectional area using FIGS. 9-11 and Equations (1)-(5). Mobility within bed 802 may also be found utilizing other variables and methods.

FIG. 13 illustrates identifying mobility in bed 802 utilizing variables M_(Bed), M_(x), and θ in relation to fluid sampling tool 100. As illustrated, M_(Bed) is the mobility of fluid in bed 802, M_(x) is the mobility of fluid moving to focus sampling probe section 708 in the x direction (e.g., M_(Bed) anisotropy is

$\left. \frac{M_{Bed}}{M_{x}} \right),$

and θ is the relative dip angle discussed above. Using Equations (1)-(5), discussed above, M is mobility and may be used to find different forms of mobility using:

$\begin{matrix} {M_{{Bed}{Anisotropy}} = \frac{F_{y}}{F_{x}}} & (6) \end{matrix}$ $\begin{matrix} {M_{Bed} = {M_{x}M_{{Bed}{Anisotropy}}}} & (7) \end{matrix}$

FIG. 14 illustrates workflow 1400 for determining bed permeability K_(Bed) of bed 802 (e.g., referring to FIG. 8 ). In examples, workflow 1400 may be performed on information handling system 122. Workflow 1400 may begin with block 1402. In block 1402, geological data is obtained for wellbore 104 (e.g., referring to FIG. 8 ). In examples, geological data of wellbore 104 may have been found in previous measurement operations in wellbore 104 (e.g., referring to FIG. 1 ) or other wellbores near wellbore 104 and stored in a database, such as cloud storage 506A-N (e.g., referring to FIG. 5 ), for reference. Generally, measurement operations may be performed by measurement tools that may comprise imaging tools, triaxial tools, and/or seismic tools. In other examples, measurement operations may be performed before fluid sampling operations performed by fluid sampling tool 100.

In block 1404, fluid sampling tool 100 may be disposed in wellbore 104 (e.g., referring to FIG. 1 ). In block 1406, geological information of wellbore 104 stored in a database, such as cloud storage 506A-N, from block 1402 may be utilized to correlate the location and/or depth of fluid sampling tool 100 to wellbore 104. In block 1408, fluid sampling locations may be identified in the fluid sampling operation using the information of wellbore 104 from block 1402. Once fluid sampling locations are identified, fluid sampling tool 100 may be moved to the identified location within wellbore 104. In block 1410, sampling probe section 708 (e.g., referring to FIG. 7 ) may be set into formation 106 at the fluid sampling locations identified in block 1408. Once sampling probe section 708 is set into formation 106, a drawdown and build up operation may be performed in block 1412. During the drawdown and build up operation, drawdown and build up measurements are taken by fluid sampling tool 100. In examples, the drawdown and build up measurements taken may be P_(x) which is pressure in the x direction with a corresponding mobility (M_(x)) in the x direction. Additionally, P_(x) is the variable P₁ in Equations (1) & (3) and M_(x) is found using Equation (7). In block 1416, bed anisotropy may be calculated using Equation (6). As illustrated, bed anisotropy calculations in block 1416 may also use θ from block 1414. In examples, θ may be found utilizing FIG. 15 .

As illustrated in FIG. 15 , θ is a relative dip angle, as discussed above. Relative dip angle, θ, is measured by identifying an angle 1500 measured from horizontal plane 804 emanating from an imager, multicomponent resistivity tool, or from probed section 708 to bed boundary 808. Referring back to FIG. 14 , using bed anisotropy calculations from block 1416, bed mobility may be calculated in block 1412 using Equation (7). Additionally, viscosity u may be assumed, measured from mud filtrate, or measured from the reservoir fluid or from a downhole fluid analysis technology. Using bed mobility and viscosity, permeability may be calculated in block 1420 by multiplying bed mobility by viscosity, as seen below.

$\begin{matrix} {M = \frac{K}{\mu}} & (8) \end{matrix}$

By identifying the permeability of bedding plane 800, personnel may be able to identify the ability for a rock formation to transmit a fluid or gas. The calculation by all formation testers does not measure permeability directly, because there isn't a way to know the actual viscosity of the fluid during the test. However, workflow 1400 shows by knowing a viscosity value, how permeability may be rendered.

FIG. 16 illustrates an example of a first sampling tool 1602 and a second sampling tool 1604 orthogonal to each other and disposed on a conveyance 102 in formation 106. In this example, either fluid sampling tools 1602, 1604 may be used to calculate the permeability of a Strata Plane 1600 and the other fluid sampling tool 1602, 1604 may be used to calculate the mobility of a bedding plane 1606. In examples, the angel of bedding plane 160 may be stratigraphic angle (p. Together, these measurements may be used to determine total mobility in a three-dimensional space. In this application, strata plane 1600 is formed from a plurality of beds 802.

FIG. 17 illustrates each fluid sampling tool 1602, 1604 disposed in wellbore 104 discussed in FIG. 16 . As mentioned above, each fluid sampling tool 1602, 1064 may view, and/or perceive, bed 802 and/or strata plane 1600 at different angles. Each fluid sampling tool 1602, 1604 may be used to identify both Strata mobility and Bed mobility. Strata mobility may be found from field knowledge of the depositional environment which is also a known paleo setting. This may also be found through a seismic earth model or from multiple imaging logs.

FIG. 18 illustrates workflow 1800 for identifying spatial permeability. In this workflow, there may be at least two fluid sampling tools 1602, 1604, (e.g., referring to FIGS. 16 and 17 ) that are orthogonal to each other. This may allow for spatial permeability to be found. In examples, workflow 1800 may be performed on information handling system 122. Workflow 1800 may begin with block 1802. In block 1802, geological data is obtained for wellbore 104 (e.g., referring to FIG. 8 ). In examples, geological data of wellbore 104 may have been found in previous measurement operations in wellbore 104 (e.g., referring to FIG. 1 ) or other wellbores near wellbore 104 and stored in a database, such as cloud storage 506A-N (e.g., referring to FIG. 5 ), for reference. Generally, measurement operations may be performed by measurement tools that may comprise imaging tools, triaxial tools, and/or seismic tools. In other examples, measurement operations may be performed before fluid sampling operations performed by fluid sampling tool 100.

In block 1804, fluid sampling tools 1602, 1604 may be disposed in wellbore 104 (e.g., referring to FIG. 1 ). In block 1806, geological information of wellbore 104 stored in a database, such as cloud storage 506A-N, from block 1802 may be utilized to correlate the location and/or depth of fluid sampling tool 100 to wellbore 104. In block 1808, fluid sampling locations may be identified in the fluid sampling operation using the information of wellbore 104 from block 1802. Once fluid sampling locations are identified, fluid sampling tools 1602, 1604 may be moved to the identified location within wellbore 104. In block 1810, sampling probe sections 708 (e.g., referring to FIG. 7 ) disposed on each fluid sampling tool 1602, 1604 may be set into formation 106 at the fluid sampling locations identified in block 1808. Once sampling probe sections 708 are set into formation 106 (e.g., referring to FIG. 1 ), a drawdown and build up operation may be performed in block 1812 to determine mobility by either fluid sampling tool 1602, 1604. Whichever fluid sampling tool 1602, 1604 performs this drawdown and build up operation may assume to be performed in the x direction. Drawdown and build up measurements from the drawdown and build up operation may measure pressure, P_(x), and mobility, M_(x), may be found using:

$\begin{matrix} {M_{x} = \frac{Cq}{P_{x} - P_{\min}}} & (9) \end{matrix}$

where C is the probe coefficient, q is the rate and P_(min) is the minimum drawdown. In block 1814, fluid sampling tools 1602, 1604 may be moved to a second location identified in block 1802. However, the second location may be adjacent to the first location and thus fluid sampling tools 1602, 1604 may not need to be moved. In block 1816, the other fluid sampling tool 1602, 1604, not utilized in block 1812 may perform a second drawdown and build up operation at a second depth. As fluid sampling tools 1602, 1604 are orthogonal to each other, the second drawdown and build up operation is in the z direction. a second probe section 708 from a second formation sampling tool 100 is moved to the depth at which a drawdown and build up was performed in block 1812. In block 1816, a second probe section 708 of fluid sampling tool 1602 or 1604 not utilized in block 1812, is set at the depth in which a second drawdown and build up may be performed. A second drawdown and build up is performed in block 1818 to determine using the methods disclosed in block 1812 and Equation (9), but this operation is for the z direction.

In block 1820, anisotropies are calculated using measurements from block 1812, 1818, and from block 1822. In block 1822, both θ and φ are found from block 1802, where θ is the relative dip angle for both fluid sampling tools 1602, 1604 and p is a stratigraphic angle. Referring back to block 1820, M_(Bed) and M_(Strat) are calculated from the anisotropic relationship shown in FIGS. 10-12 and Equations (1)-(7). Using these Equations and variables, the following may be used

$\begin{matrix} {M_{Strat} = {\frac{F_{x}}{F_{z}}M_{z}}} & (10) \end{matrix}$ $\begin{matrix} {M_{Bed} = {\frac{F_{y}}{F_{x}}M_{x}}} & (11) \end{matrix}$

In block 1824 using bed anisotropy calculations and strata anisotropy calculations from block 1824, bed mobility may be calculated using Equation (7) and strata mobility may be calculated using Equation (7) but in the stratigraphic plane solving for M_(z). In block 1826, spatial mobility may be calculated by

$\begin{matrix} {M_{spatial} = \sqrt{❘\begin{matrix} M_{x} & M_{Bed} \\ M_{Strat} & M_{z} \end{matrix}❘}} & (12) \end{matrix}$

Additionally, viscosity μ may be assumed, measured from mud filtrate, measured from the reservoir fluid or from a downhole fluid analysis technology. Using spatial mobility and viscosity, spatial permeability may be calculated in block 1828 using Equation (8), which multiplies bed mobility by viscosity. By identifying spatial permeability, personnel may be able to represent permeability in a 3D format that is suitable in Earth Models, where Geological and Geophysical calculations are represented. The benefit of this is to understand how the reservoir behaves according to geology and determine more accurate delineation of wells.

The methods and systems described above are an improvement over current technology in that these methods and systems stem from Geology, Geophysics, Petrophysics, Reservoir Engineering and Fluid mechanics. As part of the permeability calculation, prior knowledge of the formation is needed that comprises of the dip angle and bed height of the respective zone that is being tested. This information may be obtained either from Seismic knowledge of the basin, a Wireline Triaxial Resistivity (MCI) measurement, or an Imager (StrataXaminer, XRMI), which feeds into an equation that corrects existing permeability equations in the industry.

Additionally, current technology only represents permeability and mobility in a 2-dimensional format and formation testers calculate permeability with the assumption that maximum permeability (Mobility) is parallel to the probe (M_(x) or M_(z)). The workflows shown above are more representative versions of permeability calculations that align with the geology of the prospect. Geologists and Geophysicists think in a 3D space, and this workflow will allow Reservoir Engineers to translate permeability into this same representation. The end result is more accurate earth models.

Accordingly, this disclosure describes apparatus and methods that may relate to determining permeability of a formation and mobility a fluid within the formation. The apparatus, methods, and compositions may further be characterized by one or more of the following statements:

Statement 1: A method may comprise disposing a fluid sampling tool into a wellbore at a first location, taking a drawdown and build up measurement with the fluid sampling tool, and measuring a relative dip angle from the fluid sampling tool. The method may further comprise calculating a bed anisotropy from the drawdown and build up measurement and the relative dip angle, calculating a bed mobility from the bed anisotropy, and calculating a bed permeability from the bed mobility and a viscosity.

Statement 2: The method of statement 1, wherein the relative dip angle is measured from a horizontal plane emanating from the fluid sampling tool to a bed boundary.

Statement 3: The method of any previous statements 1 or 2, wherein a pressure in the x direction and a mobility in the x direction are found from the drawdown and build up measurement.

Statement 4: The method of any previous statements 1-3, wherein the viscosity is measured from a mud filtrate or reservoir fluid.

Statement 5: A method may comprise disposing a first fluid sampling tool and a second fluid sampling tool into a wellbore, wherein the first sampling tool and the second fluid sampling tool are orthogonal to each other, taking a first drawdown and build up measurement with the first fluid sampling tool in an x direction, and taking a second drawdown and build up measuring with the second fluid sampling too in the z direction. The method may further comprise measuring a first relative dip angle from the first fluid sampling tool, measuring a second relative dip angle from the second fluid sampling tool, identifying a stratigraphic angle from a database, calculating a bed anisotropy from the first drawdown and build up measurement, the second drawdown and build up measurement, the first relative dip angle, the second relative dip angle, and the stratigraphic angle, calculating a bed mobility from the bed anisotropy, and calculating a spatial permeability from the bed mobility and a viscosity.

Statement 6: The method of statement 5, wherein the first relative dip angle is measured from a first horizontal plane emanating from the first fluid sampling tool to a bed boundary.

Statement 7: The method of statement 6, wherein the second relative dip angle is measured from a second horizontal plane emanating from the second fluid sampling tool to a bed boundary.

Statement 8: The method of any previous statements 5 or 6, wherein a pressure in the x direction and a mobility in the x direction are found from the first drawdown and build up measurement.

Statement 9: The method of any previous statements 5, 6, or 8, wherein a pressure in the z direction and a mobility in the z direction are found from the second drawdown and build up measurement.

Statement 10: The method of any previous statements 5, 6, 8, or 9, wherein the viscosity is measured from a mud filtrate or reservoir fluid.

Statement 11: A system may comprise a first fluid sampling tool comprising a first sampling probe section, wherein the first sampling probe section performs a first drawdown and build up measurement with the first fluid sampling tool in an x direction and a second fluid sampling tool comprising a second sampling probe section and disposed orthogonal on a conveyance to the first fluid sampling tool, wherein the second fluid sampling tool performs a second drawdown and build up measuring with the second fluid sampling too in the z direction. The system may further comprise an information handling system connected to the first fluid sampling tool and the second fluid sampling tool and configured to identify a first relative dip angle from the first fluid sampling tool from the first fluid sampling tool or a database, identify a second relative dip angle from the second fluid sampling tool from the first fluid sampling tool or the database, and identify a stratigraphic angle from the database. The information handling system may be further configured to calculate a bed anisotropy from the first drawdown and build up measurement, the second drawdown and build up measurement, the first relative dip angle, the second relative dip angle, and the stratigraphic angle, calculate a bed mobility from the bed anisotropy; and calculate a spatial permeability from the bed mobility and a viscosity.

Statement 12. The system of statement 11, wherein the first relative dip angle is measured from a first horizontal plane emanating from the first fluid sampling tool to a bed boundary.

Statement 13: The system of statement 12, wherein the second relative dip angle is measured from a second horizontal plane emanating from the second fluid sampling tool to a bed boundary.

Statement 14: The system of any previous statements 11 or 12, wherein a pressure in the x direction and a mobility in the x direction are found from the first drawdown and build up measurement.

Statement 15: The system of any previous statements 11, 12, or 14, wherein a pressure in the z direction and a mobility in the z direction are found from the second drawdown and build up measurement.

Statement 16: The system of any previous statements 11, 12, 14, or 15, wherein the viscosity is measured from a mud filtrate or reservoir fluid.

Statement 17: The system of any previous statements 11, 12, or 14-16, wherein the spatial mobility is calculated using

${M_{spatial} = \sqrt{❘\begin{matrix} M_{x} & M_{Bed} \\ M_{Strat} & M_{z} \end{matrix}❘}},$

where M_(x) is a mobility of a fluid in the x direction, Mz is the mobility of the fluid in the z direction, M_(bed) is the mobility of the fluid in a bed, and M_(strat) is the mobility of the fluid in the stratigraphic plane.

Statement 18: The system of statement 17, wherein M_(strat) is calculated using

${M_{Strat} = {\frac{F_{x}}{F_{z}}M_{z}}},$

wherein F_(x) is a force of the fluid exerted in the x direction and Fz is a force of the fluid exerted in the z direction.

Statement 19: The system of statement 17, wherein M_(Bed) is calculated using

${M_{Bed} = {\frac{F_{y}}{F_{x}}M_{x}}},$

wherein F_(y) is a force of the fluid exerted in they direction and F_(x) is a force of the fluid exerted in the x direction.

Statement 20: The system of any previous statements 11, 12, or 14-17, wherein the database is populated from one or more measurements from one or more wellbores.

The preceding description provides various embodiments of the systems and methods of use disclosed herein which may contain different method steps and alternative combinations of components. It should be understood that, although individual embodiments may be discussed herein, the present disclosure covers all combinations of the disclosed embodiments, including, without limitation, the different component combinations, method step combinations, and properties of the system. It should be understood that the compositions and methods are described in terms of “including,” “containing,” or “including” various components or steps, the compositions and methods can also “consist essentially of” or “consist of” the various components and steps. Moreover, the indefinite articles “a” or “an,” as used in the claims, are defined herein to mean one or more than one of the element that it introduces.

For the sake of brevity, only certain ranges are explicitly disclosed herein. However, ranges from any lower limit may be combined with any upper limit to recite arrange not explicitly recited, as well as, ranges from any lower limit may be combined with any other lower limit to recite a range not explicitly recited, in the same way, ranges from any upper limit may be combined with any other upper limit to recite a range not explicitly recited. Additionally, whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any comprised range falling within the range are specifically disclosed. In particular, every range of values (of the form, “from about a to about b,” or, equivalently, “from approximately a to b,” or, equivalently, “from approximately a-b”) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values even if not explicitly recited. Thus, every point or individual value may serve as its own lower or upper limit combined with any other point or individual value or any other lower or upper limit, to recite a range not explicitly recited.

Therefore, the present embodiments are well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular embodiments disclosed above are illustrative only, and may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Although individual embodiments are discussed, the disclosure covers all combinations of all of the embodiments. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. It is therefore evident that the particular illustrative embodiments disclosed above may be altered or modified and all such variations are considered within the scope and spirit of those embodiments. If there is any conflict in the usages of a word or term in this specification and one or more patent(s) or other documents that may be incorporated herein by reference, the definitions that are consistent with this specification should be adopted. 

What is claimed is:
 1. A method comprising: disposing a fluid sampling tool into a wellbore at a first location; taking a drawdown and build up measurement with the fluid sampling tool; measuring a relative dip angle from the fluid sampling tool; calculating a bed anisotropy from the drawdown and build up measurement and the relative dip angle; calculating a bed mobility from the bed anisotropy; and calculating a bed permeability from the bed mobility and a viscosity.
 2. The method of claim 1, wherein the relative dip angle is measured from a horizontal plane emanating from the fluid sampling tool to a bed boundary.
 3. The method of claim 1, wherein a pressure in the x direction and a mobility in the x direction are found from the drawdown and build up measurement.
 4. The method of claim 1, wherein the viscosity is measured from a mud filtrate or reservoir fluid.
 5. A method comprising: disposing a first fluid sampling tool and a second fluid sampling tool into a wellbore, wherein the first sampling tool and the second fluid sampling tool are orthogonal to each other; taking a first drawdown and build up measurement with the first fluid sampling tool in an x direction; taking a second drawdown and build up measuring with the second fluid sampling too in the z direction; measuring a first relative dip angle from the first fluid sampling tool; measuring a second relative dip angle from the second fluid sampling tool; identifying a stratigraphic angle from a database; calculating a bed anisotropy from the first drawdown and build up measurement, the second drawdown and build up measurement, the first relative dip angle, the second relative dip angle, and the stratigraphic angle; calculating a bed mobility from the bed anisotropy; and calculating a spatial permeability from the bed mobility and a viscosity.
 6. The method of claim 5, wherein the first relative dip angle is measured from a first horizontal plane emanating from the first fluid sampling tool to a bed boundary.
 7. The method of claim 6, wherein the second relative dip angle is measured from a second horizontal plane emanating from the second fluid sampling tool to a bed boundary.
 8. The method of claim 5, wherein a pressure in the x direction and a mobility in the x direction are found from the first drawdown and build up measurement.
 9. The method of claim 5, wherein a pressure in the z direction and a mobility in the z direction are found from the second drawdown and build up measurement.
 10. The method of claim 5, wherein the viscosity is measured from a mud filtrate or reservoir fluid.
 11. A system comprising: a first fluid sampling tool comprising a first sampling probe section, wherein the first sampling probe section performs a first drawdown and build up measurement with the first fluid sampling tool in an x direction; a second fluid sampling tool comprising a second sampling probe section and disposed orthogonal on a conveyance to the first fluid sampling tool, wherein the second fluid sampling tool performs a second drawdown and build up measuring with the second fluid sampling too in the z direction; and an information handling system connected to the first fluid sampling tool and the second fluid sampling tool and configured to: identify a first relative dip angle from the first fluid sampling tool from the first fluid sampling tool or a database; identify a second relative dip angle from the second fluid sampling tool from the first fluid sampling tool or the database; identify a stratigraphic angle from the database; calculate a bed anisotropy from the first drawdown and build up measurement, the second drawdown and build up measurement, the first relative dip angle, the second relative dip angle, and the stratigraphic angle; calculate a bed mobility from the bed anisotropy; and calculate a spatial permeability from the bed mobility and a viscosity.
 12. The system of claim 11, wherein the first relative dip angle is measured from a first horizontal plane emanating from the first fluid sampling tool to a bed boundary.
 13. The system of claim 12, wherein the second relative dip angle is measured from a second horizontal plane emanating from the second fluid sampling tool to a bed boundary.
 14. The system of claim 11, wherein a pressure in the x direction and a mobility in the x direction are found from the first drawdown and build up measurement.
 15. The system of claim 11, wherein a pressure in the z direction and a mobility in the z direction are found from the second drawdown and build up measurement.
 16. The system of claim 11, wherein the viscosity is measured from a mud filtrate or reservoir fluid.
 17. The system of claim 11, wherein the spatial mobility is calculated using ${M_{spatial} = \sqrt{❘\begin{matrix} M_{x} & M_{Bed} \\ M_{Strat} & M_{z} \end{matrix}❘}},$ where M_(x) is a mobility of a fluid in the x direction, Mz is the mobility of the fluid in the z direction, M_(bed) is the mobility of the fluid in a bed, and M_(strat) is the mobility of the fluid in the stratigraphic plane.
 18. The system of claim 17, wherein M_(strat) is calculated using ${M_{Strat} = {\frac{F_{x}}{F_{z}}M_{z}}},$ wherein F_(x) is a force of the fluid exerted in the x direction and Fz is a force of the fluid exerted in the z direction.
 19. The system of claim 17, wherein M_(Bed) is calculated using ${M_{Bed} = {\frac{F_{y}}{F_{x}}M_{x}}},$ wherein F_(y) is a force of the fluid exerted in the y direction and F_(x) is a force of the fluid exerted in the x direction.
 20. The system of claim 11, wherein the database is populated from one or more measurements from one or more wellbores. 